def test_roll2d_polar(self):
filename = util.get_wradlib_data_file("misc/polar_dBZ_tur.gz")
data = np.loadtxt(filename)
result1 = util.roll2d_polar(data, 1, axis=0)
result2 = util.roll2d_polar(data, -1, axis=0)
result3 = util.roll2d_polar(data, 1, axis=1)
result4 = util.roll2d_polar(data, -1, axis=1)
np.testing.assert_equal(result1, np.roll(data, 1, axis=0))
np.testing.assert_equal(result2, np.roll(data, -1, axis=0))
np.testing.assert_equal(result3[:, 1:],
np.roll(data, 1, axis=1)[:, 1:])
np.testing.assert_equal(result4[:, :-1],
np.roll(data, -1, axis=1)[:, :-1])
def test_roll2d_polar(self):
filename = util.get_wradlib_data_file('misc/polar_dBZ_tur.gz')
data = np.loadtxt(filename)
result1 = util.roll2d_polar(data, 1, axis=0)
result2 = util.roll2d_polar(data, -1, axis=0)
result3 = util.roll2d_polar(data, 1, axis=1)
result4 = util.roll2d_polar(data, -1, axis=1)
np.testing.assert_equal(result1, np.roll(data, 1, axis=0))
np.testing.assert_equal(result2, np.roll(data, -1, axis=0))
np.testing.assert_equal(result3[:, 1:],
np.roll(data, 1, axis=1)[:, 1:])
np.testing.assert_equal(result4[:, :-1],
np.roll(data, -1, axis=1)[:, :-1])
def filter_window_distance(img, rscale, fsize=1500, tr1=7):
"""2d filter looking for large reflectivity gradients.
This function counts for each bin in ``img`` the percentage of surrounding
bins in a window of half size ``fsize`` which are not ``tr1`` smaller than
the central bin. The window is defined using geometrical distance.
Parameters
----------
img : array_like
2d polar data to which the filter is to be applied
rscale : float
range [m] scale of the polar grid
fsize : int
Half-size [m] of the square window surrounding the central pixel
tr1 : float
Threshold value
Returns
-------
output : array_like
an array with the same shape as ``img``, containing the
filter's results.
See Also
--------
:func:`~wradlib.clutter.filter_gabella_a` - Original version of the filter
:func:`~wradlib.clutter.filter_gabella_b` - filter using a echo area
"""
ascale = 2 * np.pi / img.shape[0]
count = np.ones(img.shape, dtype=int)
similar = np.zeros(img.shape, dtype=float)
good = np.ones(img.shape, dtype=float)
valid = (~np.isnan(img))
hole = np.sum(~valid) > 0
nr = int(round(fsize / rscale))
range_shift = range(-nr, nr + 1)
r = np.arange(img.shape[1]) * rscale + rscale / 2
adist = r * ascale
na = np.around(fsize / adist).astype(int)
max_na = img.shape[0] / 10
sa = 0
while sa < max_na:
imax = np.where(na >= sa)[0][-1] + 1
refa1 = util.roll2d_polar(img, sa, axis=0)
refa2 = util.roll2d_polar(img, -sa, axis=0)
for sr in range_shift:
refr1 = util.roll2d_polar(refa1, sr, axis=1)
similar[:, 0: imax] += (img[:, 0: imax] - refr1[:, 0: imax] < tr1)
if sa > 0:
refr2 = util.roll2d_polar(refa2, sr, axis=1)
similar[:, 0: imax] += (img[:, 0: imax] -
refr2[:, 0: imax] < tr1)
count[:, 0:imax] = 2 * sa + 1
sa += 1
similar[~valid] = np.nan
count[~valid] = -1
count[:, nr:-nr] = count[:, nr:-nr] * (2 * nr + 1)
for i in range(0, nr):
count[:, i] = count[:, i] * (nr + 1 + i)
count[:, -i - 1] = count[:, -i - 1] * (nr + 1 + i)
if hole:
good = util.filter_window_polar(valid.astype(float),
fsize, "uniform", rscale)
count = count * good
count[count == 0] = 1
similar -= 1
count -= 1
similar = similar / count
return similar
